Designing a green forward and reverse logistics network with an IoT approach considering backup suppliers and special disposal for epidemics management
| dc.contributor.author | Sina Abbasi | |
| dc.contributor.author | Sara Damavandi | |
| dc.contributor.author | Amirhossein Radmankian | |
| dc.contributor.author | Kian Zeinolabedinzadeh | |
| dc.contributor.author | Yigit Kazancoglu | |
| dc.contributor.author | Zeinolabedinzadeh, Kian | |
| dc.contributor.author | Damavandi, Sara | |
| dc.contributor.author | Abbasi, Sina | |
| dc.contributor.author | RadmanKian, Amirhossein | |
| dc.contributor.author | Kazancoglu, Yigit | |
| dc.date | JUN | |
| dc.date.accessioned | 2025-10-06T16:23:20Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | This paper proposes a mathematical model for the green forward and reverse logistics network (LN) examining the impact of epidemics such as coronavirus (COVID-19) and human metapneumovirus (HMPV) on this network. Alongside managing the network a new support center and dedicated infectious waste recycling and disposal facilities have been established. A mixed-integer linear programming (MOMILP) approach is employed for modeling a green forward and reverse LN during epidemics. This study presents two problem-solving techniques: the LP-metric method for small problems and the whale optimization algorithm (WOA) for medium and largescale issues. The positive and negative effects of epidemics on environmental and economic aspects of the objective functions were assessed. This study's contribution and novelty compared to previous research lie in the introduction of backup supply centers particularly waste disposal centers and the comparison of normal and epidemic conditions for disaster management using the IoT approach. | |
| dc.description.sponsorship | In developing a mathematical model for a closed-loop network (CLN), an integrated approach is required that takes into account cost-effectiveness, environmental sustainability, and the particular difficulties of epidemics, especially concerning the outbreak. Research highlights the importance of integrating environmentally friendly practices into RL systems and provides a solid foundation for building such a model [ 24 ]. The ability of RL to reduce environmental impact while maximizing operational efficiency is a key component. Sun & Yi [ 25 ] have developed a mathematical model that considers revenue generation, cost reduction, and environmental impact reduction under ambiguous demand situations and emphasizes the importance of integrating green practices into forward and RL. This supports the findings of Mokhlesabadi et al. [ 26 ], who emphasize the importance of incorporating environmental factors into RL models, particularly concerning greenhouse gas emissions and waste disposal. The integration of these components is crucial, especially after an epidemic, when effective resource management becomes critical. Between 2014 and 2020, the European Union has allocated more than €2.5 billion to support the social economy, and plans for the 2021–2027 period aim to expand this investment. By leveraging the multiplier effect of the Invest EU program and additional funding for social impact and innovation, the European Commission expects even greater financial support. Complementary funding from EU and national sources will also make an important contribution. It is crucial to build on previous successes to tackle demographic, environmental, and digital challenges, especially in the post-COVID-19 context. This strategy sets out the EU's efforts to support the social economy and promote sustainable SCs by 2030, which is in line with the European Pillar of Social Rights (EPSR) action plan. A key component of the social economy is the third sector, which includes a wide range of independent organizations that operate independently of government and for-profit companies [ 27 ]. | |
| dc.description.sponsorship | Invest EU; European Commission, EC | |
| dc.identifier.doi | 10.1016/j.rineng.2025.104770 | |
| dc.identifier.issn | 2590-1230 | |
| dc.identifier.scopus | 2-s2.0-105002633227 | |
| dc.identifier.uri | http://dx.doi.org/10.1016/j.rineng.2025.104770 | |
| dc.identifier.uri | https://gcris.yasar.edu.tr/handle/123456789/7808 | |
| dc.identifier.uri | https://doi.org/10.1016/j.rineng.2025.104770 | |
| dc.language.iso | English | |
| dc.publisher | ELSEVIER | |
| dc.relation.ispartof | Results in Engineering | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.source | RESULTS IN ENGINEERING | |
| dc.subject | Green logistics network, Recovery challenges, Waste management, Environmental engineering, Mixed-integer linear programming | |
| dc.subject | HUMAN METAPNEUMOVIRUS, MODEL | |
| dc.subject | Environmental Engineering | |
| dc.subject | Waste Management | |
| dc.subject | Green Logistics Network | |
| dc.subject | Mixed-Integer Linear Programming | |
| dc.subject | Recovery Challenges | |
| dc.title | Designing a green forward and reverse logistics network with an IoT approach considering backup suppliers and special disposal for epidemics management | |
| dc.type | Article | |
| dspace.entity.type | Publication | |
| gdc.author.id | Kazancoglu, Yigit/0000-0001-9199-671X | |
| gdc.author.id | Abbasi, Sina/0000-0002-8503-8010 | |
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| gdc.author.scopusid | 15848066400 | |
| gdc.author.wosid | Kazancoglu, Yigit/E-7705-2015 | |
| gdc.author.wosid | Abbasi, Sina/LZG-2357-2025 | |
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| gdc.description.departmenttemp | [Abbasi, Sina] Islamic Azad Univ, Dept Ind Engn, Lahijan Branch, Lahijan, Iran; [Damavandi, Sara] Univ G Annunzio Chieti Pescara, Dept Econ Studies, Pescara, Italy; [Radmankian, Amirhossein] Univ Texas Arlington, Dept Ind Mfg & Syst Engn, Arlington, TX USA; [Zeinolabedinzadeh, Kian] Southern Methodist Univ, Dept Operat Res & Engn Management, Dallas, TX USA; [Kazancoglu, Yigit] Yasar Univ, Logist Management Dept, Izmir, Turkiye | |
| gdc.description.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| gdc.description.startpage | 104770 | |
| gdc.description.volume | 26 | |
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| gdc.oaire.keywords | Recovery challenges | |
| gdc.oaire.keywords | Mixed-integer linear programming | |
| gdc.oaire.keywords | T | |
| gdc.oaire.keywords | Environmental engineering | |
| gdc.oaire.keywords | Waste management | |
| gdc.oaire.keywords | Green logistics network | |
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| gdc.virtual.author | Kazançoğlu, Yiğit | |
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